Abstract [en]

An estimation procedure for calibration of a low-cost inertial measurement unit (IMU), using a rigidly mounted monocular camera, is presented. The parameters of a sensor model that captures misalignments, scale and offset errors are estimated jointly with the IMU-camera coordinate transformation parameters using a recursive Sigma-Point Kalman Filter. The method requires only a simple visual calibration pattern. A simulation study indicates the filter's ability to reach subcentimeter and subdegree accuracy.

Abstract [en]

Due to the rapid error growth of navigation systems using low-cost inertial measurement units there is a need to fuse the information with complementary sensors. In this paper a monocular camera is used to aid the system. Unlike SLAM-like approaches the problem of estimating the location of each feature point viewed in a scene is avoided, instead estimated epipolar points on the image plane are used. By maintaining a buffer of past views, the method mimics a short-term visual memory which imposes multiple constraints on the estimation problem. The result is a Sigma-Point Kalman filter in square-root form with a linear and efficient time-update. A simulation study is presented indicating the filter's capacity to constrain the rate of error growth of an inertial navigation system. The filter may also find useful applications when fusing with additional sensors.

Abstract [en]

For small-scale Unmanned Aerial Vehicles (UAV) to operate indoor, in urban canyons or other scenarios where signals from global navigation satellite systems are denied or impaired, alternative estimation and control strategies must be applied. In this paper a system is proposed that estimates the self-motion and wind velocity by fusing information from airspeed sensors, an inertial measurement unit (IMU) and a monocular camera. Such estimates can be used in control systems for managing wind disturbances or chemical plume based tracking strategies. Simulation results indicate that while the inertial dead-reckoning process is subject to drift, the system is capable of separating the self-motion and wind velocity from the airspeed information.

Abstract [en]

We present a navigation system based on a monocular camera and an inertial measurement unit. The system detects visual tags and fuses the measurements on the image plane with inertial signals to perform pose estimation and localization using a Sigma-Point Kalman filter. The tags are detected by edge-based feature extraction and channel codes. During periods in which tags are not visible, epipolar constraints, arising from past views, are exploited to significantly reduce the position error growth rate. The experimental results in an office building indicate capabilities for indoor navigation.